_base_ = [
    "../_base_/models/faster_rcnn_r50_caffe_c4.py",
    "../_base_/datasets/coco_detection.py",
    "../_base_/schedules/schedule_1x.py",
    "../_base_/default_runtime.py",
]

model = dict(
    type="TridentFasterRCNN",
    pretrained="open-mmlab://detectron2/resnet50_caffe",
    backbone=dict(
        type="TridentResNet",
        trident_dilations=(1, 2, 3),
        num_branch=3,
        test_branch_idx=1,
    ),
    roi_head=dict(type="TridentRoIHead", num_branch=3, test_branch_idx=1),
    train_cfg=dict(
        rpn_proposal=dict(max_per_img=500),
        rcnn=dict(sampler=dict(num=128, pos_fraction=0.5, add_gt_as_proposals=False)),
    ),
)

# use caffe img_norm
img_norm_cfg = dict(mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(type="RandomFlip"),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="Pad", size_divisor=32),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    train=dict(pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline),
)
